parameterless-growing-som and its application to a voice instruction learning system

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ID: 256104
2010
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Ranked #22 of 23 articles by views in asse professional development conference 2017, safety 2017

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Abstract
An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. The policy is adjusted by the reward/punishment given by the user of the robot. A feeling map is also designed to express learning degrees of voice instructions. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese, and Malaysian confirmed the effectiveness of our proposed system.
Reference Key
kuremoto2010journalparameterless-growing-som Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Takashi Kuremoto;Takahito Komoto;Kunikazu Kobayashi;Masanao Obayashi
Journal asse professional development conference 2017, safety 2017
Year 2010
DOI
10.1155/2010/307293
URL
http://dx.doi.org/10.1155/2010/307293
Keywords
Mechanical engineering and machinery

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